Overview

Dataset statistics

Number of variables23
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 MiB
Average record size in memory184.0 B

Variable types

Numeric14
Categorical7
DateTime2

Alerts

Preço de Venda Anterior is highly overall correlated with Retorno sobre Investimento (ROI)High correlation
Retorno sobre Investimento (ROI) is highly overall correlated with Preço de Venda AnteriorHigh correlation
ID da Propriedade is uniformly distributedUniform
ID da Propriedade has unique valuesUnique
Área Total (em metros quadrados) has unique valuesUnique
Taxas de Juros Atuais has unique valuesUnique
Custos de Manutenção Anuais has unique valuesUnique
Taxas de Condomínio Mensais has unique valuesUnique
Impostos sobre a Propriedade has unique valuesUnique
Histórico de Valorização has unique valuesUnique
Fluxos de Caixa Anuais has unique valuesUnique
Retorno sobre Investimento (ROI) has unique valuesUnique

Reproduction

Analysis started2023-09-07 20:56:21.004394
Analysis finished2023-09-07 20:56:50.549667
Duration29.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

ID da Propriedade
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4999.5
Minimum0
Maximum9999
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2023-09-07T17:56:50.675429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile499.95
Q12499.75
median4999.5
Q37499.25
95-th percentile9499.05
Maximum9999
Range9999
Interquartile range (IQR)4999.5

Descriptive statistics

Standard deviation2886.8957
Coefficient of variation (CV)0.57743688
Kurtosis-1.2
Mean4999.5
Median Absolute Deviation (MAD)2500
Skewness0
Sum49995000
Variance8334166.7
MonotonicityStrictly increasing
2023-09-07T17:56:50.875894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
6670 1
 
< 0.1%
6663 1
 
< 0.1%
6664 1
 
< 0.1%
6665 1
 
< 0.1%
6666 1
 
< 0.1%
6667 1
 
< 0.1%
6668 1
 
< 0.1%
6669 1
 
< 0.1%
6671 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
9999 1
< 0.1%
9998 1
< 0.1%
9997 1
< 0.1%
9996 1
< 0.1%
9995 1
< 0.1%
9994 1
< 0.1%
9993 1
< 0.1%
9992 1
< 0.1%
9991 1
< 0.1%
9990 1
< 0.1%

Tipo de Imóvel
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Casa
3364 
Apartamento
3321 
Condomínio
3315 

Length

Max length11
Median length10
Mean length8.3137
Min length4

Characters and Unicode

Total characters83137
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCondomínio
2nd rowApartamento
3rd rowCasa
4th rowApartamento
5th rowApartamento

Common Values

ValueCountFrequency (%)
Casa 3364
33.6%
Apartamento 3321
33.2%
Condomínio 3315
33.1%

Length

2023-09-07T17:56:51.064361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-07T17:56:51.217978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
casa 3364
33.6%
apartamento 3321
33.2%
condomínio 3315
33.1%

Most occurring characters

ValueCountFrequency (%)
a 13370
16.1%
o 13266
16.0%
n 9951
12.0%
C 6679
8.0%
t 6642
8.0%
m 6636
8.0%
s 3364
 
4.0%
A 3321
 
4.0%
p 3321
 
4.0%
r 3321
 
4.0%
Other values (4) 13266
16.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 73137
88.0%
Uppercase Letter 10000
 
12.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 13370
18.3%
o 13266
18.1%
n 9951
13.6%
t 6642
9.1%
m 6636
9.1%
s 3364
 
4.6%
p 3321
 
4.5%
r 3321
 
4.5%
e 3321
 
4.5%
d 3315
 
4.5%
Other values (2) 6630
9.1%
Uppercase Letter
ValueCountFrequency (%)
C 6679
66.8%
A 3321
33.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 83137
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 13370
16.1%
o 13266
16.0%
n 9951
12.0%
C 6679
8.0%
t 6642
8.0%
m 6636
8.0%
s 3364
 
4.0%
A 3321
 
4.0%
p 3321
 
4.0%
r 3321
 
4.0%
Other values (4) 13266
16.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 79822
96.0%
None 3315
 
4.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 13370
16.7%
o 13266
16.6%
n 9951
12.5%
C 6679
8.4%
t 6642
8.3%
m 6636
8.3%
s 3364
 
4.2%
A 3321
 
4.2%
p 3321
 
4.2%
r 3321
 
4.2%
Other values (3) 9951
12.5%
None
ValueCountFrequency (%)
í 3315
100.0%

Localização
Categorical

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Cidade 3
1073 
Cidade 8
1071 
Cidade 6
1018 
Cidade 4
1016 
Cidade 10
991 
Other values (5)
4831 

Length

Max length9
Median length8
Mean length8.0991
Min length8

Characters and Unicode

Total characters80991
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCidade 2
2nd rowCidade 9
3rd rowCidade 4
4th rowCidade 3
5th rowCidade 10

Common Values

ValueCountFrequency (%)
Cidade 3 1073
10.7%
Cidade 8 1071
10.7%
Cidade 6 1018
10.2%
Cidade 4 1016
10.2%
Cidade 10 991
9.9%
Cidade 1 981
9.8%
Cidade 7 978
9.8%
Cidade 5 972
9.7%
Cidade 9 968
9.7%
Cidade 2 932
9.3%

Length

2023-09-07T17:56:51.373562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-07T17:56:51.544106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
cidade 10000
50.0%
3 1073
 
5.4%
8 1071
 
5.4%
6 1018
 
5.1%
4 1016
 
5.1%
10 991
 
5.0%
1 981
 
4.9%
7 978
 
4.9%
5 972
 
4.9%
9 968
 
4.8%

Most occurring characters

ValueCountFrequency (%)
d 20000
24.7%
C 10000
12.3%
i 10000
12.3%
a 10000
12.3%
e 10000
12.3%
10000
12.3%
1 1972
 
2.4%
3 1073
 
1.3%
8 1071
 
1.3%
6 1018
 
1.3%
Other values (6) 5857
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 50000
61.7%
Decimal Number 10991
 
13.6%
Uppercase Letter 10000
 
12.3%
Space Separator 10000
 
12.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1972
17.9%
3 1073
9.8%
8 1071
9.7%
6 1018
9.3%
4 1016
9.2%
0 991
9.0%
7 978
8.9%
5 972
8.8%
9 968
8.8%
2 932
8.5%
Lowercase Letter
ValueCountFrequency (%)
d 20000
40.0%
i 10000
20.0%
a 10000
20.0%
e 10000
20.0%
Uppercase Letter
ValueCountFrequency (%)
C 10000
100.0%
Space Separator
ValueCountFrequency (%)
10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 60000
74.1%
Common 20991
 
25.9%

Most frequent character per script

Common
ValueCountFrequency (%)
10000
47.6%
1 1972
 
9.4%
3 1073
 
5.1%
8 1071
 
5.1%
6 1018
 
4.8%
4 1016
 
4.8%
0 991
 
4.7%
7 978
 
4.7%
5 972
 
4.6%
9 968
 
4.6%
Latin
ValueCountFrequency (%)
d 20000
33.3%
C 10000
16.7%
i 10000
16.7%
a 10000
16.7%
e 10000
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80991
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 20000
24.7%
C 10000
12.3%
i 10000
12.3%
a 10000
12.3%
e 10000
12.3%
10000
12.3%
1 1972
 
2.4%
3 1073
 
1.3%
8 1071
 
1.3%
6 1018
 
1.3%
Other values (6) 5857
 
7.2%

Número de Quartos
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5228
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2023-09-07T17:56:51.746565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7169085
Coefficient of variation (CV)0.48737043
Kurtosis-1.2803966
Mean3.5228
Median Absolute Deviation (MAD)2
Skewness-0.011566165
Sum35228
Variance2.9477749
MonotonicityNot monotonic
2023-09-07T17:56:51.897134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
6 1742
17.4%
2 1666
16.7%
4 1656
16.6%
5 1654
16.5%
1 1648
16.5%
3 1634
16.3%
ValueCountFrequency (%)
1 1648
16.5%
2 1666
16.7%
3 1634
16.3%
4 1656
16.6%
5 1654
16.5%
6 1742
17.4%
ValueCountFrequency (%)
6 1742
17.4%
5 1654
16.5%
4 1656
16.6%
3 1634
16.3%
2 1666
16.7%
1 1648
16.5%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
3
2518 
2
2507 
4
2490 
1
2485 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row2
4th row2
5th row3

Common Values

ValueCountFrequency (%)
3 2518
25.2%
2 2507
25.1%
4 2490
24.9%
1 2485
24.9%

Length

2023-09-07T17:56:52.054741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-07T17:56:52.192373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 2518
25.2%
2 2507
25.1%
4 2490
24.9%
1 2485
24.9%

Most occurring characters

ValueCountFrequency (%)
3 2518
25.2%
2 2507
25.1%
4 2490
24.9%
1 2485
24.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2518
25.2%
2 2507
25.1%
4 2490
24.9%
1 2485
24.9%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 2518
25.2%
2 2507
25.1%
4 2490
24.9%
1 2485
24.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 2518
25.2%
2 2507
25.1%
4 2490
24.9%
1 2485
24.9%

Área Total (em metros quadrados)
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.42471
Minimum50.029975
Maximum299.99237
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2023-09-07T17:56:52.366906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50.029975
5-th percentile62.398304
Q1111.30489
median174.20364
Q3237.26656
95-th percentile287.66745
Maximum299.99237
Range249.9624
Interquartile range (IQR)125.96167

Descriptive statistics

Standard deviation72.288497
Coefficient of variation (CV)0.41443955
Kurtosis-1.2073878
Mean174.42471
Median Absolute Deviation (MAD)62.936804
Skewness0.022387893
Sum1744247.1
Variance5225.6268
MonotonicityNot monotonic
2023-09-07T17:56:52.565375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
132.5164833 1
 
< 0.1%
141.2527997 1
 
< 0.1%
180.0529384 1
 
< 0.1%
128.2495943 1
 
< 0.1%
152.3296062 1
 
< 0.1%
60.98447424 1
 
< 0.1%
102.9274619 1
 
< 0.1%
55.42767237 1
 
< 0.1%
73.4354479 1
 
< 0.1%
280.7208024 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
50.02997545 1
< 0.1%
50.06434721 1
< 0.1%
50.08660563 1
< 0.1%
50.10271433 1
< 0.1%
50.18461476 1
< 0.1%
50.23092467 1
< 0.1%
50.24697697 1
< 0.1%
50.27180469 1
< 0.1%
50.27733408 1
< 0.1%
50.27859945 1
< 0.1%
ValueCountFrequency (%)
299.992371 1
< 0.1%
299.9850678 1
< 0.1%
299.9422512 1
< 0.1%
299.9368812 1
< 0.1%
299.9148751 1
< 0.1%
299.8811765 1
< 0.1%
299.8739193 1
< 0.1%
299.8520367 1
< 0.1%
299.8483253 1
< 0.1%
299.8410294 1
< 0.1%

Idade da Propriedade
Real number (ℝ)

Distinct49
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.1161
Minimum1
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2023-09-07T17:56:52.756863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q113
median25
Q338
95-th percentile47
Maximum49
Range48
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.23054
Coefficient of variation (CV)0.56659036
Kurtosis-1.2232842
Mean25.1161
Median Absolute Deviation (MAD)13
Skewness-0.0073249912
Sum251161
Variance202.50827
MonotonicityNot monotonic
2023-09-07T17:56:52.952340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
12 236
 
2.4%
26 227
 
2.3%
43 226
 
2.3%
42 222
 
2.2%
38 222
 
2.2%
13 216
 
2.2%
10 216
 
2.2%
39 215
 
2.1%
27 215
 
2.1%
40 215
 
2.1%
Other values (39) 7790
77.9%
ValueCountFrequency (%)
1 193
1.9%
2 212
2.1%
3 208
2.1%
4 193
1.9%
5 213
2.1%
6 210
2.1%
7 194
1.9%
8 212
2.1%
9 202
2.0%
10 216
2.2%
ValueCountFrequency (%)
49 212
2.1%
48 205
2.1%
47 206
2.1%
46 205
2.1%
45 208
2.1%
44 205
2.1%
43 226
2.3%
42 222
2.2%
41 210
2.1%
40 215
2.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Boa
2534 
Ruim
2506 
Média
2485 
Excelente
2475 

Length

Max length9
Median length5
Mean length5.2326
Min length3

Characters and Unicode

Total characters52326
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBoa
2nd rowRuim
3rd rowRuim
4th rowExcelente
5th rowExcelente

Common Values

ValueCountFrequency (%)
Boa 2534
25.3%
Ruim 2506
25.1%
Média 2485
24.9%
Excelente 2475
24.8%

Length

2023-09-07T17:56:53.135822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-07T17:56:53.280463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
boa 2534
25.3%
ruim 2506
25.1%
média 2485
24.9%
excelente 2475
24.8%

Most occurring characters

ValueCountFrequency (%)
e 7425
14.2%
a 5019
 
9.6%
i 4991
 
9.5%
B 2534
 
4.8%
o 2534
 
4.8%
R 2506
 
4.8%
u 2506
 
4.8%
m 2506
 
4.8%
d 2485
 
4.7%
é 2485
 
4.7%
Other values (7) 17335
33.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 42326
80.9%
Uppercase Letter 10000
 
19.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 7425
17.5%
a 5019
11.9%
i 4991
11.8%
o 2534
 
6.0%
u 2506
 
5.9%
m 2506
 
5.9%
d 2485
 
5.9%
é 2485
 
5.9%
x 2475
 
5.8%
c 2475
 
5.8%
Other values (3) 7425
17.5%
Uppercase Letter
ValueCountFrequency (%)
B 2534
25.3%
R 2506
25.1%
M 2485
24.9%
E 2475
24.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 52326
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 7425
14.2%
a 5019
 
9.6%
i 4991
 
9.5%
B 2534
 
4.8%
o 2534
 
4.8%
R 2506
 
4.8%
u 2506
 
4.8%
m 2506
 
4.8%
d 2485
 
4.7%
é 2485
 
4.7%
Other values (7) 17335
33.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49841
95.3%
None 2485
 
4.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 7425
14.9%
a 5019
 
10.1%
i 4991
 
10.0%
B 2534
 
5.1%
o 2534
 
5.1%
R 2506
 
5.0%
u 2506
 
5.0%
m 2506
 
5.0%
d 2485
 
5.0%
M 2485
 
5.0%
Other values (6) 14850
29.8%
None
ValueCountFrequency (%)
é 2485
100.0%

Amenidades
Categorical

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Garagem, Piscina
866 
Garagem, Vista panorâmica
853 
Piscina, Garagem
849 
Piscina, Vista panorâmica
838 
Jardim, Vista panorâmica
837 
Other values (7)
5757 

Length

Max length25
Median length24
Mean length19.9978
Min length15

Characters and Unicode

Total characters199978
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJardim, Piscina
2nd rowJardim, Garagem
3rd rowGaragem, Jardim
4th rowGaragem, Jardim
5th rowPiscina, Garagem

Common Values

ValueCountFrequency (%)
Garagem, Piscina 866
8.7%
Garagem, Vista panorâmica 853
8.5%
Piscina, Garagem 849
8.5%
Piscina, Vista panorâmica 838
8.4%
Jardim, Vista panorâmica 837
8.4%
Piscina, Jardim 835
8.3%
Vista panorâmica, Piscina 831
8.3%
Jardim, Piscina 824
8.2%
Vista panorâmica, Garagem 822
8.2%
Garagem, Jardim 818
8.2%
Other values (2) 1627
16.3%

Length

2023-09-07T17:56:53.451977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
piscina 5043
20.2%
garagem 5025
20.1%
vista 4991
20.0%
panorâmica 4991
20.0%
jardim 4941
19.8%

Most occurring characters

ValueCountFrequency (%)
a 35007
17.5%
i 25009
12.5%
14991
 
7.5%
r 14957
 
7.5%
m 14957
 
7.5%
s 10034
 
5.0%
n 10034
 
5.0%
c 10034
 
5.0%
, 10000
 
5.0%
P 5043
 
2.5%
Other values (10) 49912
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 154987
77.5%
Uppercase Letter 20000
 
10.0%
Space Separator 14991
 
7.5%
Other Punctuation 10000
 
5.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 35007
22.6%
i 25009
16.1%
r 14957
9.7%
m 14957
9.7%
s 10034
 
6.5%
n 10034
 
6.5%
c 10034
 
6.5%
e 5025
 
3.2%
g 5025
 
3.2%
t 4991
 
3.2%
Other values (4) 19914
12.8%
Uppercase Letter
ValueCountFrequency (%)
P 5043
25.2%
G 5025
25.1%
V 4991
25.0%
J 4941
24.7%
Space Separator
ValueCountFrequency (%)
14991
100.0%
Other Punctuation
ValueCountFrequency (%)
, 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 174987
87.5%
Common 24991
 
12.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 35007
20.0%
i 25009
14.3%
r 14957
 
8.5%
m 14957
 
8.5%
s 10034
 
5.7%
n 10034
 
5.7%
c 10034
 
5.7%
P 5043
 
2.9%
G 5025
 
2.9%
e 5025
 
2.9%
Other values (8) 39862
22.8%
Common
ValueCountFrequency (%)
14991
60.0%
, 10000
40.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194987
97.5%
None 4991
 
2.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 35007
18.0%
i 25009
12.8%
14991
 
7.7%
r 14957
 
7.7%
m 14957
 
7.7%
s 10034
 
5.1%
n 10034
 
5.1%
c 10034
 
5.1%
, 10000
 
5.1%
P 5043
 
2.6%
Other values (9) 44921
23.0%
None
ValueCountFrequency (%)
â 4991
100.0%

Preço de Venda Anterior
Real number (ℝ)

HIGH CORRELATION 

Distinct9985
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2547788.4
Minimum50561
Maximum4999713
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2023-09-07T17:56:53.691337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50561
5-th percentile309899.15
Q11301099.5
median2573457.5
Q33766462
95-th percentile4754152.8
Maximum4999713
Range4949152
Interquartile range (IQR)2465362.5

Descriptive statistics

Standard deviation1422560.9
Coefficient of variation (CV)0.55835127
Kurtosis-1.1901187
Mean2547788.4
Median Absolute Deviation (MAD)1233890.5
Skewness-0.028053806
Sum2.5477884 × 1010
Variance2.0236794 × 1012
MonotonicityNot monotonic
2023-09-07T17:56:53.908755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
903255 2
 
< 0.1%
1188172 2
 
< 0.1%
4008657 2
 
< 0.1%
3499980 2
 
< 0.1%
2425587 2
 
< 0.1%
3927641 2
 
< 0.1%
174806 2
 
< 0.1%
2821064 2
 
< 0.1%
1381146 2
 
< 0.1%
4895384 2
 
< 0.1%
Other values (9975) 9980
99.8%
ValueCountFrequency (%)
50561 1
< 0.1%
50861 1
< 0.1%
51087 1
< 0.1%
53198 1
< 0.1%
53565 1
< 0.1%
53631 1
< 0.1%
53943 1
< 0.1%
55430 1
< 0.1%
55757 1
< 0.1%
58077 1
< 0.1%
ValueCountFrequency (%)
4999713 1
< 0.1%
4999666 1
< 0.1%
4999263 1
< 0.1%
4999102 1
< 0.1%
4998376 1
< 0.1%
4997977 1
< 0.1%
4996865 1
< 0.1%
4996466 1
< 0.1%
4995617 1
< 0.1%
4995059 1
< 0.1%
Distinct7272
Distinct (%)72.7%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Minimum1980-01-02 00:00:00
Maximum2023-12-28 00:00:00
2023-09-07T17:56:54.219952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:54.416425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Sim
5047 
Não
4953 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNão
2nd rowNão
3rd rowNão
4th rowSim
5th rowSim

Common Values

ValueCountFrequency (%)
Sim 5047
50.5%
Não 4953
49.5%

Length

2023-09-07T17:56:54.602927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-07T17:56:54.730558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
sim 5047
50.5%
não 4953
49.5%

Most occurring characters

ValueCountFrequency (%)
S 5047
16.8%
i 5047
16.8%
m 5047
16.8%
N 4953
16.5%
ã 4953
16.5%
o 4953
16.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 20000
66.7%
Uppercase Letter 10000
33.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 5047
25.2%
m 5047
25.2%
ã 4953
24.8%
o 4953
24.8%
Uppercase Letter
ValueCountFrequency (%)
S 5047
50.5%
N 4953
49.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 30000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 5047
16.8%
i 5047
16.8%
m 5047
16.8%
N 4953
16.5%
ã 4953
16.5%
o 4953
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25047
83.5%
None 4953
 
16.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 5047
20.2%
i 5047
20.2%
m 5047
20.2%
N 4953
19.8%
o 4953
19.8%
None
ValueCountFrequency (%)
ã 4953
100.0%

Taxas de Juros Atuais
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0191612
Minimum2.0005724
Maximum5.9998766
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2023-09-07T17:56:54.894148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0005724
5-th percentile2.2194968
Q13.0253364
median4.0389695
Q34.9953185
95-th percentile5.8025812
Maximum5.9998766
Range3.9993042
Interquartile range (IQR)1.9699821

Descriptive statistics

Standard deviation1.1458843
Coefficient of variation (CV)0.28510534
Kurtosis-1.181104
Mean4.0191612
Median Absolute Deviation (MAD)0.98816305
Skewness-0.021905092
Sum40191.612
Variance1.3130509
MonotonicityNot monotonic
2023-09-07T17:56:55.105583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.768412977 1
 
< 0.1%
5.864338165 1
 
< 0.1%
3.431830421 1
 
< 0.1%
5.949160387 1
 
< 0.1%
2.652809574 1
 
< 0.1%
5.641514137 1
 
< 0.1%
2.115837583 1
 
< 0.1%
2.942977085 1
 
< 0.1%
4.785595111 1
 
< 0.1%
4.773862045 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
2.000572391 1
< 0.1%
2.001053149 1
< 0.1%
2.001233192 1
< 0.1%
2.002083342 1
< 0.1%
2.002526597 1
< 0.1%
2.002614399 1
< 0.1%
2.003170141 1
< 0.1%
2.004176952 1
< 0.1%
2.004233655 1
< 0.1%
2.005166728 1
< 0.1%
ValueCountFrequency (%)
5.999876608 1
< 0.1%
5.999321971 1
< 0.1%
5.998424707 1
< 0.1%
5.997926654 1
< 0.1%
5.997690757 1
< 0.1%
5.997559774 1
< 0.1%
5.997402011 1
< 0.1%
5.996972547 1
< 0.1%
5.996684588 1
< 0.1%
5.996631277 1
< 0.1%

Custos de Manutenção Anuais
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2575.668
Minimum100.74002
Maximum4999.9062
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2023-09-07T17:56:55.313000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100.74002
5-th percentile355.45213
Q11373.1778
median2579.1333
Q33807.5477
95-th percentile4760.536
Maximum4999.9062
Range4899.1662
Interquartile range (IQR)2434.3698

Descriptive statistics

Standard deviation1410.1589
Coefficient of variation (CV)0.54749249
Kurtosis-1.194048
Mean2575.668
Median Absolute Deviation (MAD)1221.1767
Skewness-0.024184879
Sum25756680
Variance1988548.1
MonotonicityNot monotonic
2023-09-07T17:56:55.515487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
143.2110906 1
 
< 0.1%
4024.757527 1
 
< 0.1%
1748.969237 1
 
< 0.1%
3787.014242 1
 
< 0.1%
1031.915601 1
 
< 0.1%
723.6072693 1
 
< 0.1%
1004.552108 1
 
< 0.1%
2694.926964 1
 
< 0.1%
3419.529095 1
 
< 0.1%
582.3877829 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
100.7400172 1
< 0.1%
100.9073733 1
< 0.1%
102.3146468 1
< 0.1%
103.3411314 1
< 0.1%
103.9291634 1
< 0.1%
103.9391474 1
< 0.1%
105.7794728 1
< 0.1%
106.6301897 1
< 0.1%
108.0783343 1
< 0.1%
108.9056146 1
< 0.1%
ValueCountFrequency (%)
4999.906235 1
< 0.1%
4999.766924 1
< 0.1%
4999.553809 1
< 0.1%
4997.560696 1
< 0.1%
4996.903569 1
< 0.1%
4996.555228 1
< 0.1%
4995.635205 1
< 0.1%
4995.631541 1
< 0.1%
4995.608585 1
< 0.1%
4995.577046 1
< 0.1%

Taxas de Condomínio Mensais
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean276.07205
Minimum50.005069
Maximum499.97038
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2023-09-07T17:56:55.713957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50.005069
5-th percentile72.736811
Q1164.84844
median276.0354
Q3388.17671
95-th percentile477.36374
Maximum499.97038
Range449.96531
Interquartile range (IQR)223.32827

Descriptive statistics

Standard deviation129.46891
Coefficient of variation (CV)0.46896782
Kurtosis-1.1923987
Mean276.07205
Median Absolute Deviation (MAD)111.47305
Skewness-0.00049048624
Sum2760720.5
Variance16762.198
MonotonicityNot monotonic
2023-09-07T17:56:55.917386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
339.415339 1
 
< 0.1%
135.3145377 1
 
< 0.1%
363.2617612 1
 
< 0.1%
241.8507575 1
 
< 0.1%
252.5152955 1
 
< 0.1%
213.5145154 1
 
< 0.1%
348.2562374 1
 
< 0.1%
191.7659685 1
 
< 0.1%
136.0380641 1
 
< 0.1%
406.3093932 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
50.00506921 1
< 0.1%
50.0166546 1
< 0.1%
50.02038182 1
< 0.1%
50.05618813 1
< 0.1%
50.07982918 1
< 0.1%
50.14630773 1
< 0.1%
50.15289404 1
< 0.1%
50.21073772 1
< 0.1%
50.23063884 1
< 0.1%
50.23664789 1
< 0.1%
ValueCountFrequency (%)
499.9703751 1
< 0.1%
499.9600306 1
< 0.1%
499.914181 1
< 0.1%
499.9036029 1
< 0.1%
499.8879881 1
< 0.1%
499.8470332 1
< 0.1%
499.833329 1
< 0.1%
499.7550325 1
< 0.1%
499.7351845 1
< 0.1%
499.7031839 1
< 0.1%

Impostos sobre a Propriedade
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2755.6276
Minimum500.14366
Maximum4999.812
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2023-09-07T17:56:56.126824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum500.14366
5-th percentile721.88652
Q11635.1543
median2744.0391
Q33877.1666
95-th percentile4774.2716
Maximum4999.812
Range4499.6683
Interquartile range (IQR)2242.0124

Descriptive statistics

Standard deviation1298.6374
Coefficient of variation (CV)0.47126737
Kurtosis-1.1995897
Mean2755.6276
Median Absolute Deviation (MAD)1120.3179
Skewness-0.0040166998
Sum27556276
Variance1686459
MonotonicityNot monotonic
2023-09-07T17:56:56.321333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3478.119342 1
 
< 0.1%
3601.375184 1
 
< 0.1%
1447.726041 1
 
< 0.1%
3339.752304 1
 
< 0.1%
961.8023228 1
 
< 0.1%
2900.509047 1
 
< 0.1%
2947.223927 1
 
< 0.1%
1222.97801 1
 
< 0.1%
4362.948425 1
 
< 0.1%
2769.974023 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
500.1436563 1
< 0.1%
500.3037817 1
< 0.1%
500.7945611 1
< 0.1%
501.3044766 1
< 0.1%
501.5392372 1
< 0.1%
501.6479774 1
< 0.1%
501.6834149 1
< 0.1%
502.8304571 1
< 0.1%
504.0098407 1
< 0.1%
504.3014854 1
< 0.1%
ValueCountFrequency (%)
4999.811952 1
< 0.1%
4999.693212 1
< 0.1%
4999.633425 1
< 0.1%
4998.884294 1
< 0.1%
4998.581906 1
< 0.1%
4997.755666 1
< 0.1%
4997.464953 1
< 0.1%
4996.974493 1
< 0.1%
4996.623324 1
< 0.1%
4995.298285 1
< 0.1%

Histórico de Valorização
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.299023
Minimum0.10000306
Maximum0.49995762
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2023-09-07T17:56:56.518804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.10000306
5-th percentile0.11862847
Q10.19758835
median0.29741821
Q30.40081344
95-th percentile0.47979531
Maximum0.49995762
Range0.39995456
Interquartile range (IQR)0.20322509

Descriptive statistics

Standard deviation0.11631834
Coefficient of variation (CV)0.38899462
Kurtosis-1.2190172
Mean0.299023
Median Absolute Deviation (MAD)0.10171365
Skewness0.0063400948
Sum2990.23
Variance0.013529956
MonotonicityNot monotonic
2023-09-07T17:56:56.732206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1308608439 1
 
< 0.1%
0.2582687161 1
 
< 0.1%
0.1341726427 1
 
< 0.1%
0.1189671105 1
 
< 0.1%
0.2221380288 1
 
< 0.1%
0.1664799689 1
 
< 0.1%
0.422992636 1
 
< 0.1%
0.4210188527 1
 
< 0.1%
0.1412439448 1
 
< 0.1%
0.2845010092 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
0.1000030613 1
< 0.1%
0.1000064443 1
< 0.1%
0.1000119694 1
< 0.1%
0.1000659294 1
< 0.1%
0.1000676273 1
< 0.1%
0.1000968662 1
< 0.1%
0.1000980848 1
< 0.1%
0.1000992743 1
< 0.1%
0.1001944103 1
< 0.1%
0.1002884366 1
< 0.1%
ValueCountFrequency (%)
0.4999576169 1
< 0.1%
0.4999481209 1
< 0.1%
0.4999411794 1
< 0.1%
0.4997964596 1
< 0.1%
0.4997912963 1
< 0.1%
0.4997802256 1
< 0.1%
0.4996646366 1
< 0.1%
0.4995987482 1
< 0.1%
0.4995635439 1
< 0.1%
0.4995597177 1
< 0.1%

Fluxos de Caixa Anuais
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25455.534
Minimum1003.4996
Maximum49991.564
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2023-09-07T17:56:56.943670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1003.4996
5-th percentile3349.9124
Q113057.946
median25635.702
Q337835.854
95-th percentile47397.381
Maximum49991.564
Range48988.064
Interquartile range (IQR)24777.908

Descriptive statistics

Standard deviation14184.466
Coefficient of variation (CV)0.55722526
Kurtosis-1.2080636
Mean25455.534
Median Absolute Deviation (MAD)12408.908
Skewness-0.012058094
Sum2.5455534 × 108
Variance2.0119908 × 108
MonotonicityNot monotonic
2023-09-07T17:56:57.161059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47250.32375 1
 
< 0.1%
10269.10107 1
 
< 0.1%
38318.34826 1
 
< 0.1%
25804.52839 1
 
< 0.1%
28916.83228 1
 
< 0.1%
28478.40512 1
 
< 0.1%
40794.77324 1
 
< 0.1%
41140.35576 1
 
< 0.1%
22432.39793 1
 
< 0.1%
15646.74024 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1003.499644 1
< 0.1%
1004.293073 1
< 0.1%
1016.293662 1
< 0.1%
1036.818163 1
< 0.1%
1042.852941 1
< 0.1%
1046.399005 1
< 0.1%
1051.966339 1
< 0.1%
1052.251334 1
< 0.1%
1053.595173 1
< 0.1%
1068.309871 1
< 0.1%
ValueCountFrequency (%)
49991.56366 1
< 0.1%
49990.71478 1
< 0.1%
49990.18152 1
< 0.1%
49989.47341 1
< 0.1%
49977.95932 1
< 0.1%
49976.20755 1
< 0.1%
49973.40656 1
< 0.1%
49967.63078 1
< 0.1%
49966.81188 1
< 0.1%
49965.32061 1
< 0.1%

Retorno sobre Investimento (ROI)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1021347.2
Minimum22016.399
Maximum2002233.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2023-09-07T17:56:57.395433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22016.399
5-th percentile126280.9
Q1522881.35
median1031706.6
Q31508920.1
95-th percentile1903643.8
Maximum2002233.9
Range1980217.5
Interquartile range (IQR)986038.76

Descriptive statistics

Standard deviation569027.47
Coefficient of variation (CV)0.55713424
Kurtosis-1.1900573
Mean1021347.2
Median Absolute Deviation (MAD)493935.13
Skewness-0.028053114
Sum1.0213472 × 1010
Variance3.2379226 × 1011
MonotonicityNot monotonic
2023-09-07T17:56:57.619863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1706092.663 1
 
< 0.1%
179037.3426 1
 
< 0.1%
1957968.418 1
 
< 0.1%
199846.9009 1
 
< 0.1%
841513.8988 1
 
< 0.1%
764743.0533 1
 
< 0.1%
637024.8811 1
 
< 0.1%
746621.1568 1
 
< 0.1%
1809588.644 1
 
< 0.1%
1255128.681 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
22016.39861 1
< 0.1%
22342.14089 1
< 0.1%
22659.97783 1
< 0.1%
22705.22205 1
< 0.1%
22735.69469 1
< 0.1%
23603.05429 1
< 0.1%
24271.01406 1
< 0.1%
24525.75255 1
< 0.1%
24574.08933 1
< 0.1%
24755.37535 1
< 0.1%
ValueCountFrequency (%)
2002233.928 1
< 0.1%
2002225.978 1
< 0.1%
2002103.362 1
< 0.1%
2002009.614 1
< 0.1%
2001963.985 1
< 0.1%
2001508.747 1
< 0.1%
2001246.826 1
< 0.1%
2000626.709 1
< 0.1%
2000555.686 1
< 0.1%
2000461.446 1
< 0.1%
Distinct4117
Distinct (%)41.2%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Minimum2010-01-01 00:00:00
Maximum2023-12-28 00:00:00
2023-09-07T17:56:57.834287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:58.032756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Fonte dos Dados
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Redfin
3425 
Zillow
3326 
Kaggle
3249 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters60000
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKaggle
2nd rowRedfin
3rd rowRedfin
4th rowRedfin
5th rowZillow

Common Values

ValueCountFrequency (%)
Redfin 3425
34.2%
Zillow 3326
33.3%
Kaggle 3249
32.5%

Length

2023-09-07T17:56:58.206292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-07T17:56:58.341930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
redfin 3425
34.2%
zillow 3326
33.3%
kaggle 3249
32.5%

Most occurring characters

ValueCountFrequency (%)
l 9901
16.5%
i 6751
11.3%
e 6674
11.1%
g 6498
10.8%
R 3425
 
5.7%
d 3425
 
5.7%
f 3425
 
5.7%
n 3425
 
5.7%
Z 3326
 
5.5%
o 3326
 
5.5%
Other values (3) 9824
16.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 50000
83.3%
Uppercase Letter 10000
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 9901
19.8%
i 6751
13.5%
e 6674
13.3%
g 6498
13.0%
d 3425
 
6.9%
f 3425
 
6.9%
n 3425
 
6.9%
o 3326
 
6.7%
w 3326
 
6.7%
a 3249
 
6.5%
Uppercase Letter
ValueCountFrequency (%)
R 3425
34.2%
Z 3326
33.3%
K 3249
32.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 60000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 9901
16.5%
i 6751
11.3%
e 6674
11.1%
g 6498
10.8%
R 3425
 
5.7%
d 3425
 
5.7%
f 3425
 
5.7%
n 3425
 
5.7%
Z 3326
 
5.5%
o 3326
 
5.5%
Other values (3) 9824
16.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 9901
16.5%
i 6751
11.3%
e 6674
11.1%
g 6498
10.8%
R 3425
 
5.7%
d 3425
 
5.7%
f 3425
 
5.7%
n 3425
 
5.7%
Z 3326
 
5.5%
o 3326
 
5.5%
Other values (3) 9824
16.4%

Valor aluguel
Real number (ℝ)

Distinct4015
Distinct (%)40.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2787.3727
Minimum501
Maximum4999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2023-09-07T17:56:58.537407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum501
5-th percentile736.9
Q11661
median2805
Q33934
95-th percentile4777
Maximum4999
Range4498
Interquartile range (IQR)2273

Descriptive statistics

Standard deviation1301.523
Coefficient of variation (CV)0.46693542
Kurtosis-1.216073
Mean2787.3727
Median Absolute Deviation (MAD)1139
Skewness-0.036999631
Sum27873727
Variance1693962.2
MonotonicityNot monotonic
2023-09-07T17:56:58.734851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2474 8
 
0.1%
4981 8
 
0.1%
1894 8
 
0.1%
2813 8
 
0.1%
1041 8
 
0.1%
4321 7
 
0.1%
2319 7
 
0.1%
4102 7
 
0.1%
3860 7
 
0.1%
1805 7
 
0.1%
Other values (4005) 9925
99.2%
ValueCountFrequency (%)
501 1
 
< 0.1%
502 1
 
< 0.1%
503 1
 
< 0.1%
504 2
 
< 0.1%
505 1
 
< 0.1%
506 3
< 0.1%
507 4
< 0.1%
508 5
0.1%
509 1
 
< 0.1%
510 3
< 0.1%
ValueCountFrequency (%)
4999 3
< 0.1%
4998 4
< 0.1%
4997 2
< 0.1%
4995 2
< 0.1%
4994 4
< 0.1%
4993 4
< 0.1%
4992 1
 
< 0.1%
4991 2
< 0.1%
4990 2
< 0.1%
4989 1
 
< 0.1%

Preço de Venda Atual
Real number (ℝ)

Distinct9991
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2517869.4
Minimum51106
Maximum4998676
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2023-09-07T17:56:58.934317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum51106
5-th percentile315149.85
Q11281462
median2506496
Q33748362
95-th percentile4754763
Maximum4998676
Range4947570
Interquartile range (IQR)2466900

Descriptive statistics

Standard deviation1424460.1
Coefficient of variation (CV)0.56574025
Kurtosis-1.2014886
Mean2517869.4
Median Absolute Deviation (MAD)1235726.5
Skewness0.017005195
Sum2.5178694 × 1010
Variance2.0290865 × 1012
MonotonicityNot monotonic
2023-09-07T17:56:59.266429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1836840 2
 
< 0.1%
707641 2
 
< 0.1%
1247854 2
 
< 0.1%
4405261 2
 
< 0.1%
2709835 2
 
< 0.1%
509097 2
 
< 0.1%
816935 2
 
< 0.1%
3794560 2
 
< 0.1%
4797143 2
 
< 0.1%
141667 1
 
< 0.1%
Other values (9981) 9981
99.8%
ValueCountFrequency (%)
51106 1
< 0.1%
51548 1
< 0.1%
51553 1
< 0.1%
51836 1
< 0.1%
52327 1
< 0.1%
52617 1
< 0.1%
52937 1
< 0.1%
53262 1
< 0.1%
54102 1
< 0.1%
54516 1
< 0.1%
ValueCountFrequency (%)
4998676 1
< 0.1%
4998511 1
< 0.1%
4998486 1
< 0.1%
4998230 1
< 0.1%
4997930 1
< 0.1%
4997922 1
< 0.1%
4996274 1
< 0.1%
4995623 1
< 0.1%
4995265 1
< 0.1%
4994808 1
< 0.1%

Interactions

2023-09-07T17:56:47.776081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:22.965422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:24.846365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:26.779197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:28.638225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:30.476339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:32.309439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:34.368932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:36.225964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:38.041111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:39.866233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:41.968611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:43.900415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:45.953925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:48.023416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:23.098072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:24.971031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:26.909877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:28.764918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:30.604002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:32.444077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:34.497558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:36.352633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:38.167772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:40.005859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:42.095271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:44.043062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:46.077622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:48.151050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:23.234675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:25.093733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:27.039531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:28.890551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:30.730662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:32.582679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:34.628209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:36.479287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:38.296400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:40.145487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:42.226925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:44.185683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:46.203290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:48.282725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:23.366353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:25.229340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:27.166161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:29.021230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:30.862279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:32.719312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:34.758894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:36.605954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:38.424088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:40.286113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:42.356571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:44.328300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:46.333936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:48.411386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:23.493981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:25.364016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:27.295845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:29.144901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:30.988970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:32.856973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:34.887516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:36.732609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:38.550748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:40.541397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:42.487225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:44.470921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:46.460605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:48.549015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:23.625656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:25.493664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:27.426493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:29.272562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:31.117623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:32.993609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:35.019163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:36.860270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:38.679407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:40.680055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:42.617873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:44.614536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:46.590251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:48.700612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:23.775229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:25.637282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:27.573105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:29.416176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:31.260244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:33.144207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:35.166799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:37.002887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:38.821995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:40.836641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:42.791381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:44.776104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:46.732866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:48.838212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:23.909896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:25.765934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:27.702754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:29.547821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:31.388901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:33.388553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:35.293461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:37.131545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:38.947689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:40.976262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:42.936991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:44.923708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:46.860531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:48.974880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:24.035562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:25.887611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:27.829412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:29.673460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:31.513569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:33.521197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:35.420119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:37.250225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:39.073353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:41.110906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:43.070657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:45.066326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:46.985167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:49.097547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:24.159230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:26.013245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:27.955080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:29.800119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:31.637236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:33.652848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:35.546752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:37.374894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:39.196026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:41.247537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:43.206300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:45.203959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:47.107872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:49.246150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:24.308831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:26.245624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:28.102690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:29.950755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:31.783815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:33.806435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:35.692391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:37.518508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:39.340637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:41.399134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:43.361891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:45.366524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:47.255475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:49.372811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:24.438455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:26.376274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:28.233336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:30.077405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:31.910503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:33.944038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:35.825037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:37.647165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:39.468297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:41.538761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:43.491537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:45.510140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:47.381109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:49.524408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:24.595058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:26.524906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:28.381943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:30.225013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:32.059108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:34.099652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:35.971644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:37.792775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:39.618897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:41.696339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:43.643132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:45.669716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:47.528714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:49.650072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:24.715742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:26.652535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:28.507604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:30.347654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:32.180753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:34.231301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:36.096314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:37.915450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:39.739542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:41.829952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:43.769797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:45.806319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-07T17:56:47.647397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-09-07T17:56:59.446947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ID da PropriedadeNúmero de QuartosÁrea Total (em metros quadrados)Idade da PropriedadePreço de Venda AnteriorTaxas de Juros AtuaisCustos de Manutenção AnuaisTaxas de Condomínio MensaisImpostos sobre a PropriedadeHistórico de ValorizaçãoFluxos de Caixa AnuaisRetorno sobre Investimento (ROI)Valor aluguelPreço de Venda AtualTipo de ImóvelLocalizaçãoNúmero de BanheirosCondição da PropriedadeAmenidadesHistórico de AluguelFonte dos Dados
ID da Propriedade1.0000.0110.0130.005-0.004-0.0100.0150.023-0.017-0.0030.005-0.0040.0110.0030.0210.0150.0110.0000.0090.0300.004
Número de Quartos0.0111.0000.0120.0200.0020.011-0.0080.0040.001-0.0150.0150.0020.007-0.0150.0090.0000.0000.0000.0000.0220.016
Área Total (em metros quadrados)0.0130.0121.000-0.0020.0010.003-0.006-0.0090.0050.003-0.0300.001-0.0160.0200.0160.0060.0000.0000.0110.0000.000
Idade da Propriedade0.0050.020-0.0021.000-0.0010.0070.005-0.0060.0040.010-0.001-0.001-0.010-0.0040.0230.0180.0000.0170.0130.0110.000
Preço de Venda Anterior-0.0040.0020.001-0.0011.0000.006-0.001-0.0080.0080.0020.0011.0000.005-0.0050.0000.0150.0060.0220.0070.0000.003
Taxas de Juros Atuais-0.0100.0110.0030.0070.0061.0000.006-0.0020.0160.010-0.0100.006-0.0010.0070.0200.0090.0110.0160.0120.0000.016
Custos de Manutenção Anuais0.015-0.008-0.0060.005-0.0010.0061.000-0.0090.0070.0150.009-0.0010.0070.0040.0050.0130.0000.0000.0000.0000.000
Taxas de Condomínio Mensais0.0230.004-0.009-0.006-0.008-0.002-0.0091.000-0.027-0.0050.005-0.0080.010-0.0030.0250.0130.0070.0000.0000.0170.000
Impostos sobre a Propriedade-0.0170.0010.0050.0040.0080.0160.007-0.0271.0000.001-0.0060.008-0.0130.0040.0000.0000.0000.0000.0070.0000.021
Histórico de Valorização-0.003-0.0150.0030.0100.0020.0100.015-0.0050.0011.000-0.0020.0020.011-0.0040.0220.0000.0130.0040.0000.0240.000
Fluxos de Caixa Anuais0.0050.015-0.030-0.0010.001-0.0100.0090.005-0.006-0.0021.0000.0010.008-0.0060.0000.0000.0000.0000.0000.0100.007
Retorno sobre Investimento (ROI)-0.0040.0020.001-0.0011.0000.006-0.001-0.0080.0080.0020.0011.0000.005-0.0050.0000.0140.0020.0200.0070.0000.006
Valor aluguel0.0110.007-0.016-0.0100.005-0.0010.0070.010-0.0130.0110.0080.0051.000-0.0100.0000.0000.0000.0060.0000.0090.022
Preço de Venda Atual0.003-0.0150.020-0.004-0.0050.0070.004-0.0030.004-0.004-0.006-0.005-0.0101.0000.0000.0090.0000.0110.0000.0000.000
Tipo de Imóvel0.0210.0090.0160.0230.0000.0200.0050.0250.0000.0220.0000.0000.0000.0001.0000.0000.0100.0000.0170.0000.004
Localização0.0150.0000.0060.0180.0150.0090.0130.0130.0000.0000.0000.0140.0000.0090.0001.0000.0020.0140.0000.0000.017
Número de Banheiros0.0110.0000.0000.0000.0060.0110.0000.0070.0000.0130.0000.0020.0000.0000.0100.0021.0000.0000.0180.0070.000
Condição da Propriedade0.0000.0000.0000.0170.0220.0160.0000.0000.0000.0040.0000.0200.0060.0110.0000.0140.0001.0000.0000.0090.000
Amenidades0.0090.0000.0110.0130.0070.0120.0000.0000.0070.0000.0000.0070.0000.0000.0170.0000.0180.0001.0000.0040.000
Histórico de Aluguel0.0300.0220.0000.0110.0000.0000.0000.0170.0000.0240.0100.0000.0090.0000.0000.0000.0070.0090.0041.0000.000
Fonte dos Dados0.0040.0160.0000.0000.0030.0160.0000.0000.0210.0000.0070.0060.0220.0000.0040.0170.0000.0000.0000.0001.000

Missing values

2023-09-07T17:56:49.866463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-07T17:56:50.324239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ID da PropriedadeTipo de ImóvelLocalizaçãoNúmero de QuartosNúmero de BanheirosÁrea Total (em metros quadrados)Idade da PropriedadeCondição da PropriedadeAmenidadesPreço de Venda AnteriorData de Venda AnteriorHistórico de AluguelTaxas de Juros AtuaisCustos de Manutenção AnuaisTaxas de Condomínio MensaisImpostos sobre a PropriedadeHistórico de ValorizaçãoFluxos de Caixa AnuaisRetorno sobre Investimento (ROI)Data de Inclusão no DatasetFonte dos DadosValor aluguelPreço de Venda Atual
00CondomínioCidade 223132.51648322BoaJardim, Piscina42632352008-06-22Não2.768413143.211091339.4153393478.1193420.13086147250.3237491.706093e+062015-11-03Kaggle860.02958033.0
11ApartamentoCidade 913277.61787639RuimJardim, Garagem2715172001-05-13Não2.3053823175.433219273.780401978.7136510.30953240263.7677101.112115e+052010-04-24Redfin3661.01944660.0
22CasaCidade 44273.94387647RuimGaragem, Jardim26111652006-09-16Não5.9118544975.962194197.527322572.6936810.18582928273.3721801.046947e+062023-07-20Redfin2215.02102332.0
33ApartamentoCidade 352149.99517712ExcelenteGaragem, Jardim27836052017-01-28Sim3.7305684608.266956236.3398194540.8810770.38316113499.6080111.117232e+062015-04-23Redfin4744.03277862.0
44ApartamentoCidade 1013237.06094835ExcelentePiscina, Garagem30003122009-11-27Sim5.3088401229.545915117.1877003597.9722230.21283212317.7722521.202105e+062016-03-02Zillow2958.0517393.0
55CasaCidade 443171.02845121RuimGaragem, Vista panorâmica17986572014-11-02Não3.2914112649.516340350.3913134314.9255590.31483013762.0642047.216678e+052010-04-24Redfin2260.01716584.0
66ApartamentoCidade 944276.19629649RuimGaragem, Piscina36941841981-04-01Não5.4901132208.246593134.6395191186.9592270.4918875909.0350861.479432e+062020-04-07Redfin2237.03344543.0
77ApartamentoCidade 101372.90240227RuimPiscina, Jardim30259382002-04-10Não4.8736043116.994442202.1374531238.8611110.13003421696.5043711.213364e+062020-04-13Zillow4720.0741595.0
88CondomínioCidade 85483.39102848ExcelenteJardim, Garagem5878832001-12-13Não4.9717852515.373414384.4022281898.8522970.41198217313.8727052.368038e+052022-03-28Kaggle1604.01109507.0
99CondomínioCidade 124282.15071335MédiaJardim, Piscina4513152003-08-10Não2.3787164399.569208418.1935361004.6449680.2524711386.9895151.840348e+052018-04-21Redfin4956.02023914.0
ID da PropriedadeTipo de ImóvelLocalizaçãoNúmero de QuartosNúmero de BanheirosÁrea Total (em metros quadrados)Idade da PropriedadeCondição da PropriedadeAmenidadesPreço de Venda AnteriorData de Venda AnteriorHistórico de AluguelTaxas de Juros AtuaisCustos de Manutenção AnuaisTaxas de Condomínio MensaisImpostos sobre a PropriedadeHistórico de ValorizaçãoFluxos de Caixa AnuaisRetorno sobre Investimento (ROI)Data de Inclusão no DatasetFonte dos DadosValor aluguelPreço de Venda Atual
99909990ApartamentoCidade 132188.93382635BoaPiscina, Jardim44236091996-08-23Não3.4179594349.316011196.7832544429.4522430.17696828609.9513871.771613e+062014-02-24Redfin891.02212517.0
99919991CasaCidade 834188.80304228BoaPiscina, Garagem18783602003-04-22Sim3.6274044708.766543360.9778084096.5650800.2937671913.5020687.552038e+052010-11-01Redfin4888.03747763.0
99929992CondomínioCidade 553213.39336211MédiaJardim, Vista panorâmica25639571994-07-10Não2.4992754715.14418664.2010434309.5391010.4682738417.1707061.029157e+062018-04-05Zillow4192.065553.0
99939993ApartamentoCidade 106361.3779252MédiaVista panorâmica, Piscina11009062004-03-01Não4.3219734743.464468309.0148011741.6102840.23918536965.2394924.437177e+052022-04-19Redfin4280.02993619.0
99949994CasaCidade 323134.31868610MédiaJardim, Piscina42683072006-09-16Não4.3482431679.627978422.7607974355.3279850.42657849976.2075531.708822e+062015-07-20Redfin1217.01840124.0
99959995CasaCidade 261183.21630946RuimGaragem, Jardim31238481995-04-25Sim4.2127464822.579775154.415832681.5283930.49514835419.0630511.251642e+062021-06-02Redfin1313.04660457.0
99969996CasaCidade 944157.00849841BoaGaragem, Vista panorâmica6863292022-07-07Não5.5538281296.651818102.647440977.8015610.14779717680.6117792.758138e+052013-12-05Redfin1862.04304307.0
99979997CasaCidade 664296.85431149ExcelenteGaragem, Piscina48786102004-03-15Não4.1068173211.269911314.5156774584.9582180.16123116307.3485101.953689e+062011-02-26Redfin1805.02571834.0
99989998CasaCidade 753243.87682414ExcelenteVista panorâmica, Piscina18650272014-03-20Sim5.2650233702.116691369.8732171293.8697530.31690516460.2085807.487648e+052018-01-22Redfin3562.0545221.0
99999999CondomínioCidade 861194.4764298RuimVista panorâmica, Piscina44192202015-12-23Não3.7338232189.432347122.0613294656.4516030.49648914726.3707141.770679e+062011-07-11Zillow4533.0582748.0